With the application of multisource images in many areas, image fusion has been an attractive and important technique of image processing and pattern recognition. Image fusion is to combine two or more source images into one single combined image containing significant information of respective source images. The application areas of image fusion include exposure fusion [1-2], remote sensing [3-4], medical imaging [5], quality and defect detection and biometric [6]. Particularly in mobile platform, there is a strong desire to fuse multisource images to obtain a better illustration and explanation about the sensed scene.
In image fusion, it is always desired that the fused image has both high spatial and high spectral resolutions so as to obtain a better description and interpretation of a sensed scene. However, due to physical or observation constraints, high spatial resolution and high spectral resolution are typically not simultaneously available. For example, high resolution images possess high spatial resolution but poor spectral resolution, while multispectral images possess high spectral resolution but low spatial resolution.
Image fusion should follow some fusion rules to construct a synthetic image. In this aspect, a variety of methods have been proposed to fuse high resolution images and multispectral data. For example, Nunez et al. [8] fuse a spatial resolution high resolution image (SPOT) with a low spatial resolution multispectral image (Landsat Thematic Mapper (TM)) by using the additive wavelet (AW) algorithm. The wavelet low frequency portion of the SPOT high resolution image is substituted by the bands of TM image.
However, those fusion methods perform fusion pointwisely and just use the local information of the neighborhood domain. Another shortcoming is that multiscale method preserves more spectral information but ignores some spatial information [9].